A general overview of malnutrition in normal kidney function and in chronic kidney disease

NDT Plus Advance Access published September 16, 2009 NDT Plus (2009) 1 of 7 doi: 10.1093/ndtplus/sfp128 Invited Comment A general overview of malnut...
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NDT Plus Advance Access published September 16, 2009 NDT Plus (2009) 1 of 7 doi: 10.1093/ndtplus/sfp128

Invited Comment

A general overview of malnutrition in normal kidney function and in chronic kidney disease Anne-Elisabeth Heng1,2 and No¨el J. M. Cano3,4,5 1

CHU Clermont-Ferrand, Service de N´ephrologie, Hˆopital G Montpied, F-63003 Clermont-Ferrand, 2 Inra, UMR 1019 Nutrition Humaine, F-63122 Saint Gen`es Champanelle, 3 CHU Clermont-Ferrand, Service de Nutrition, Hˆopital G Montpied, F-63003, Clermont-Ferrand, 4 Univ Clermont1, UFR M´edecine, UMR 1019 Nutrition Humaine, F-63000, Clermont-Ferrand and 5 CRNH Auvergne, F-63009, Clermont-Ferrand, France Correspondence and offprint requests to: No¨el Cano; E-mail: [email protected]

Keywords: chronic kidney disease; haemodialysis; nutrition; peritoneal dialysis; survival

In adult patients, the main nutrition-related diseases are obesity, diabetes type 2 and dyslipidaemia and on the other hand malnutrition. Malnutrition can be defined as the imbalance between intake and requirement which results in altered metabolism, impaired function and loss of body mass [1]. Such a definition includes both the so-called protein– energy malnutrition (PEM) and inadequate micronutrient status. Although macro- and micronutrient deficiencies are most often associated, most studies on ‘malnutrition’ or undernutrition usually address PEM. The purpose of the present review is to give a general overview of PEM and its assessment methods in normal kidney function and in renal patients. Recently, an expert panel from the International Society of Renal Nutrition and Metabolism proposed the term ‘protein energy wasting’ (PEW) to designate malnutrition in kidney diseases and gave its definition and a pathophysiological basis [2].

Protein–energy malnutrition in normal kidney function Protein–energy malnutrition subtypes Several PEM subtypes have been described according to the respective deficiencies in protein and/or energy intakes, the presence of inflammation and the speed of the constitution of PEM. Acute PEM, which is observed in stressed conditions, such as severe acute kidney disease, will not be developed in this review [3]. Marasmus is the consequence of a prolonged partial starvation with a similar reduction of both protein and energy intakes. In marasmic patients, hormonal changes, mainly characterized by a decrease in the insulin/glucagon ratio, make it possible to adapt to starvation. This adaptation consists in a progressive switch in the use of energy substrates from glucose to ketone bodies, together with a decrease in

gluconeogenesis from amino acids and an increase in fatty acid release from adipose tissue. The clinical picture of marasmus is a depletion of both fat and fat-free mass without noticeable water retention. In these patients, albumin synthesis is preserved and no oedema is observed. Kwashiorkor is the consequence of prolonged insufficient intakes predominating on protein supply. Most often, kwashiorkor is associated with inflammation due to chronic intestinal or cutaneous infections and malabsorption. In this setting, because of inflammatory cytokines and hormonalrelated changes, the decrease in the insulin/glucagon ratio cannot occur. The adaptation to starvation is no more possible, leading to an increased use of muscle protein stores for gluconeogenesis. Such protein malnutrition is associated with hypoalbuminaemia, oedemas and sometimes ascites. In clinical practice, a lot of intermediate nutritional pictures can be observed between marasmus and kwashiorkor. In patients with chronic disease, the presence of both insufficient intakes and inflammation can be responsible for protein malnutrition mimicking kwashiorkor. The prevalence and causes of PEM in chronic diseases are given in Table 1. In hospitalized patients, PEM is found in 20– 50% of patients depending on criteria for malnutrition and clinical settings [4]. Protein malnutrition, as characterized by the presence of hypoalbuminaemia, is associated with higher rates of complications and mortality together with an increase of hospitalization length and overall costs [4–6]. Causes of malnutrition in chronic diseases The causes of PEM in chronic diseases are given in Table 1. Schematically, PEM can be explained by insufficient nutrient intake (primary malnutrition) and/or an increase in nutrient needs due to disease-related metabolic abnormalities leading to increased energy expenditure, increased protein degradation and negative protein balance (secondary malnutrition). Insufficient nutrient intake can be due to mechanical obstructions of the digestive tract, disease-induced anorexia, depression and low socioeconomic status.

 C The Author 2009. Published by Oxford University Press [on behalf of ERA-EDTA]. All rights reserved. For Permissions, please email: [email protected]

BMI Lean body mass Nutritional predictors of prognosis

PEM, protein–energy malnutrition; BMI, body mass index; REE, resting energy expenditure; ID, insufficiently documented; HIV, human immune deficiency virus.

Serum albumin Body cell mass Arm muscle circumference REE Body cell mass Body weight loss

Anorexia Inconstantly increased Increased Inflammation Insulin resistance Alcohol Malabsorption Pancreatic insufficiency Inadequate intakes Increased ID Inflammation Insulin resistance Hypoxia

Anorexia Inconstantly increased Inconstantly increased Inflammation Insulin resistance Acidosis Abnormal growth factor metabolism Hyperparathyroidism Androgenopaenia BMI Body weight loss Serum albumin Serum transthyretin (prealbumin)

Anorexia Unchanged ID Inflammation ID Malabsorption Cell hypoxia

Nutritional assessment

Insufficient intakes REE Protein turnover Inflammatory status Insulin sensitivity Other Prevalence of PEM Causes of malnutrition

Chronic liver disease Heart failure Chronic kidney disease Respiratory failure

Table 1. Prevalence, causes and prognostic influence of protein–energy malnutrition in chronic diseases

Anorexia Increased Increased Inflammation Insulin resistance Intestinal disease Respiratory failure

A.-E. Heng and N. J. M. Cano

HIV infection

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Numerous methods have been used for assessing nutritional status. Dietician consultation is of primary importance in chronically ill patients both for assessing nutritional intakes and providing nutritional counselling. Among anthropometric measurements, body weight and height, and body mass index (BMI, body weight/height2 ) are routinely used. BMI and body weight loss were reported as independent markers of survival in chronic respiratory and renal diseases [2,7]. In the absence of water retention, triceps skinfold thickness (TSF) reflects fat stores and arm muscle circumference (AMC = arm circumference − 3.14 × TSF) muscle mass. TSF and AMC should be considered according to reference values for age and gender [8]. Serum concentrations of albumin and transthyretin (prealbumin) are determined both by nutritional variables, namely protein–energy intakes, and by non-nutritional variables such as inflammation, liver function, hydration status, gender, age and, regarding to serum albumin, urinary losses [9,10]. However, in chronically depleted patients, these serum proteins also reflect protein intake and nutritional status [2,11]. Both serum albumin and transthyretin exhibit a high prognostic value in chronically ill patients as well as in patient undergoing surgical interventions [10,12,13]. The subjective global nutritional assessment (SGA), using variables derived from history and physical examination, has been validated in numerous medical and surgical settings [14]. Body impedance analysis (BIA) using the 50-kHz frequency makes it possible to assess fat mass and fat-free mass. Multifrequency BIA is required for measuring the ratio of extracellular water to total body water in subjects with end-stage renal disease [15]. However, BIA results should be interpreted with caution in patients with body water changes. DEXA is the reference method for body composition measurement, which makes it possible to measure fat mass and fat-free mass, including mineral mass [16]. The association of DEXA with multi-frequency BIA makes it possible to assess body cell mass. Among these methods used to assess nutritional status, BMI, body weight loss and serum protein measurements appeared more useful for detecting high-risk patients requiring nutritional intervention. Buzby et al. proposed a nutritional risk index (NRI) based on serum albumin and the ratio of present to usual body weight: NRI = 1.519 × serum albumin + 0.417 × (present/usual by weight/100). According to this index, patients can be classified as non-malnourished (NRI > 97.5), moderately malnourished (97.5 > NRI > 83.5) and severely malnourished (NRI < 83.5). This index, validated in surgical patients, is now widely used for the nutritional assessment of hospitalized patient [12].

Particularities of chronic kidney disease patients Prevalence of protein–energy wasting The term ‘protein–energy wasting’ (PEW) in acute, and chronic kidney disease (CKD) has been recently proposed

A general overview of malnutrition in normal kidney function and in chronic kidney disease

by an expert panel for the loss of body protein mass and fuel reserves. PEW should be diagnosed if three characteristics are present: low serum levels of albumin, transthyretin, or cholesterol; reduced body mass defined by low or reduced BMI, fat mass or weight loss with a reduced intake of protein and energy; and reduced muscle mass defined by muscle wasting or sarcopaenia and reduced mid-AMC [2]. The prevalence of PEW progressively increases during the evolution of CKD. It has been reported that 40% of the patients present with symptoms of PEW at the entrance in dialysis [17]. Adequate data are lacking to compare nutritional status in peritoneal dialysis (PD) and haemodialysis (HD). In one Italian report, maintenance HD patients >76 years were more likely to be malnourished than PD patients. In patients 1.0 g/kg ideal BW/day • Serum albumin: 1 month after beginning of HD and three monthly thereafter. Serum albumin should be >40 g/L by bromocresol green method • Serum transthyretin should be >300 mg/L • Serum cholesterol should be less than minimal laboratory threshold value

HD, haemodialysis; PD, peritoneal dialysis; SGA, subjective global assessment; nPNA, normalized equivalent of total nitrogen appearance; BW, body weight.

water to total body water in subjects with end-stage renal disease [63]. In HD patients, body cell mass estimated by BIA was shown to be highly correlated with body cell mass determined by dual-energy x-ray absorptiometry (DEXA) and NaBr [64]. BIA measurements vary to a considerable extent according to the point in time chosen for performing BIA, but remain constant and highly reproducible during the first 120 min following the end of HD, that is, in a dryweight state [65,66]. BIA results should be interpreted with caution in patients with body water changes. Moreover, in order to ensure reproducible results, a standardization of formulas used for calculation as well as a clear definition of the acceptable times to perform BIA is required. Similarly, in PD patient BIA offers reliable estimates of total body composition but population-specific equations are needed. Because DEXA estimates lean body mass as body mass minus fat and bone masses, it can be noticed that the estimation of lean body mass includes possible changes in the hydration state. Nevertheless, in HD as well as in PD patients, according to present guidelines, DEXA remains the reference method for the precise measurements of body composition and bone mineral density [45,67–70]. The precision of DEXA for fat mass measurement is ∼3%, in HD and PD patients [67]. The recommended variables for the routine follow-up of dialysis patients are given in Table 2.

Conclusion PEM is widespread among patients with chronic organ failure, usually as a consequence of both low nutrient intakes and abnormal nutrient metabolism. In these patients, nutritional status is an independent predictor of morbidity and mortality. Therefore, the detection and treatment of PEM appears as a major challenge in the care of chronically ill patients. In routine clinical practice, the detection of malnourished patients most often relies on simple tools such as dietary records, height and weight measurements, history of weight changes, BMI and measurement of serum albu-

min and transthyretin. The subjective global assessment of nutritional status has been validated in several chronic diseases, including CKD. In selected cases, body composition measurements can be useful. In CKD patients, PEW has recently been defined as the association of low serum markers of malnutrition, together with indicators of decreased fat and fat-free mass. The fact that PEW compromising the 1-year survival is found in ∼25% of dialysis patients underlines the necessity of a close dietary and nutritional management of these patients. Conflict of interest statement. None declared.

References 1. Kinosian B, Jeejeebhoy KN. What is malnutrition? Does it matter? Nutrition 1995; 11: 196–197 2. Fouque D, Kalantar-Zadeh K, Kopple J et al. A proposed nomenclature and diagnostic criteria for protein-energy wasting in acute and chronic kidney disease. Kidney Int 2008; 73: 391–398 3. Jackson NC, Carroll PV, Russell-Jones DL et al. The metabolic consequences of critical illness: acute effects on glutamine and protein metabolism. Am J Physiol 1999; 276: E163–E170 4. Norman K, Pichard C, Lochs H et al. Prognostic impact of diseaserelated malnutrition. Clin Nutr 2008; 27: 5–15 5. McClave SA. Differentiating subtypes (hypoalbuminemic versus marasmic) of protein-calorie malnutrition: incidence and clinical significance in a university hospital setting. JPEN J Parenter Enteral Nutr 1992; 16: 337–342 6. Pichard C, Kyle UG, Morabia A et al. Nutritional assessment: lean body mass depletion at hospital admission is associated with an increased length of stay. Am J Clin Nutr 2004; 79: 613–618 7. Celli BR, Cote CG, Marin JM et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004; 350: 1005–1012 8. Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr 1981; 34: 2540–2545 9. Rothschild MA, Oratz M, Schreiber SS. Serum albumin. Hepatology 1988; 8: 385–401 10. Ingenbleek Y, Young V. Transthyretin (prealbumin) in health and disease: nutritional implications. Annu Rev Nutr 1994; 14: 495–533 11. Aparicio M, Cano N, Chauveau P et al. French Study Group for Nutrition in Dialysis. Nutritional status of haemodialysis patients: a

6

12. 13.

14.

15.

16.

17. 18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

A.-E. Heng and N. J. M. Cano French national cooperative study. Nephrol Dial Transplant 1999; 14: 1679–1686 Buzby GP, Mullen JL, Matthews DC et al. Prognostic nutritional index in gastrointestinal surgery. Am J Surg 1980; 139: 160–167 Cano NJ. Metabolism and clinical interest of serum transthyretin (prealbumin) in dialysis patients. Clin Chem Lab Med 2002; 40: 1313– 1319 Detsky AS, McLaughin JR, Baker JP. What is subjective global assessment of nutritional status ? JPEN J Parenter Enteral Nutr 1987; 11: 8–13 Kyle UG, Bosaeus I, De Lorenzo AD et al. Bioelectrical impedance analysis: part I. Review of principles and methods. Clin Nutr 2004; 23: 1226–1243 Haarbo J, Gotfredsen A, Hassager C et al. Validation of body composition by dual energy X-ray absorptiometry (DEXA). Clin Physiol 1991; 11: 331–341 Ikizler TA, Hakim RM. Nutrition in end-stage renal disease. Kidney Int 1996; 50: 343–357 Cianciaruso B, Brunori G, Kopple JD et al. Cross-sectional comparison of malnutrition in continuous ambulatory peritoneal dialysis and hemodialysis patients. Am J Kidney Dis 1995; 26: 475–486 Port FK, Pisoni RL, Bragg-Gresham JL et al. DOPPS estimates of patient life years attributable to modifiable hemodialysis practices in the United States. Blood Purif 2004; 22: 175–180 Cano NJ, Roth H, Aparicio M et al. Malnutrition in hemodialysis diabetic patients: evaluation and prognostic influence. Kidney Int 2002; 62: 593–601 Pupim LB, Heimburger O, Qureshi AR et al. Accelerated lean body mass loss in incident chronic dialysis patients with diabetes mellitus. Kidney Int 2005; 68: 2368–2374 Cano N, Fernandez JP, Lacombe P et al. Statistical selection of nutritional parameters in hemodialyzed patients. Kidney Int 1987; 32(Suppl 22): S178–S180 Owen WF Jr., Lew NL, Liu Y et al. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med 1993; 329: 1001–1006 Avram MM, Mittman N, Bonomini L et al. Markers for survival in dialysis: a seven-year prospective study. Am J Kidney Dis 1995; 26: 209–219 Combe C, Chauveau P, Laville M et al. Influence of nutritional factors and hemodialysis adequacy on the survival of 1610 French patients. Am J Kidney Dis 2001; 37: S81–S88 Chertow GM, Ackert K, Lew NL et al. Prealbumin is as important as albumin in the nutritional assessment of hemodialysis patients. Kidney Int 2000; 58: 2512–2517 Chertow GM, Goldstein-Fuchs DJ, Lazarus JM et al. Prealbumin, mortality, and cause-specific hospitalization in hemodialysis patients. Kidney Int 2005; 68: 2794–2800 Chauveau P, Nguyen H, Combe C et al. Dialyzer membrane permeability and survival in hemodialysis patients. Am J Kidney Dis 2005; 45: 565–571 Chung SH, Lindholm B, Lee HB. Influence of initial nutritional status on continuous ambulatory peritoneal dialysis patient survival. Perit Dial Int 2000; 20: 19–26 Stenvinkel P, Barany P, Chung SH et al. A comparative analysis of nutritional parameters as predictors of outcome in male and female ESRD patients. Nephrol Dial Transplant 2002; 17: 1266–1274 Leavey SF, McCullough K, Hecking E et al. Body mass index and mortality in ‘healthier’ as compared with ‘sicker’ haemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant 2001; 16: 2386–2394 Leavey SF, Strawderman RL, Jones CA et al. Simple nutritional indicators as independent predictors of mortality in hemodialysis patients. Am J Kidney Dis 1998; 31: 997–1006 Port FK, Ashby VB, Dhingra RK et al. Dialysis dose and body mass index are strongly associated with survival in hemodialysis patients. J Am Soc Nephrol 2002; 13: 1061–1066 Kalantar-Zadeh K, Block G, Humphreys MH et al. A low, rather than a high, total plasma homocysteine is an indicator of poor out-

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50. 51.

52. 53.

54.

55.

56.

come in hemodialysis patients. J Am Soc Nephrol 2004; 15: 442– 453 Port FK, Hulbert-Shearon TE, Wolfe RA et al. Predialysis blood pressure and mortality risk in a national sample of maintenance hemodialysis patients. Am J Kidney Dis 1999; 33: 507–517 Nishizawa Y, Shoji T, Ishimura E et al. Paradox of risk factors for cardiovascular mortality in uremia: is a higher cholesterol level better for atherosclerosis in uremia? Am J Kidney Dis 2001; 38: S4–S7 Kalantar-Zadeh K, Block G, Humphreys MH et al. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 2003; 63: 793–808 Kalantar-Zadeh K, Abbott KC, Salahudeen AK et al. Survival advantages of obesity in dialysis patients. Am J Clin Nutr 2005; 81: 543–554 Kalantar-Zadeh K, Horwich TB, Oreopoulos A et al. Risk factor paradox in wasting diseases. Curr Opin Clin Nutr Metab Care 2007; 10: 433–442 de Mutsert R, Snijder MB, Van Der Sman-de Beer F et al. Association between body mass index and mortality is similar in the hemodialysis population and the general population at high age and equal duration of follow-up. J Am Soc Nephrol 2007; 18: 967–974 Chailleux E, Laaban JP, Veale D. Prognostic value of nutritional depletion in patients with COPD treated by long-term oxygen therapy: data from the ANTADIR Observatory. Chest 2003; 123: 1460– 1466 Honda H, Qureshi AR, Axelsson J et al. Obese sarcopenia in patients with end-stage renal disease is associated with inflammation and increased mortality. Am J Clin Nutr 2007; 86: 633–638 Beddhu S, Pappas LM, Ramkumar N et al. Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol 2003; 14: 2366–2372 Kakiya R, Shoji T, Tsujimoto Y et al. Body fat mass and lean mass as predictors of survival in hemodialysis patients. Kidney Int 2006; 70: 549–556 K/DOQI, National Kidney Foundation. Clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis 2000; 35: S1–S140 Toigo G, Aparicio M, Attman PO et al. Expert Working Group report on nutrition in adult patients with renal insufficiency (part 1 of 2). Clin Nutr 2000; 19: 197–207 Heimburger O, Lonnqvist F, Danielsson A et al. Serum immunoreactive leptin concentration and its relation to the body fat content in chronic renal failure. J Am Soc Nephrol 1997; 8: 1423–1430 Heimburger O, Qureshi AR, Blaner WS et al. Hand-grip muscle strength, lean body mass, and plasma proteins as markers of nutritional status in patients with chronic renal failure close to start of dialysis therapy. Am J Kidney Dis 2000; 36: 1213–1225 Wang AY, Sea MM, Ho ZS et al. Evaluation of handgrip strength as a nutritional marker and prognostic indicator in peritoneal dialysis patients. Am J Clin Nutr 2005; 81: 79–86 Jacob V, LeCarpentier JE, Salzano S et al. IGF-1, a marker of undernutrition in hemodialysis patients. Am J Clin Nutr 1990; 52: 39–44 Goldwasser P, Michel MA, Collier J et al. Prealbumin and lipoprotein(a) in hemodialysis: relationships with patient and vascular access survival. Am J Kidney Dis 1993; 22: 215–225 Fouque D, Vennegoor M, ter Wee P et al. EBPG guideline on nutrition. Nephrol Dial Transplant 2007; 22(Suppl 2): ii45–ii87 Cano NJ, Fouque D, Roth H et al. Intradialytic parenteral nutrition does not improve survival in malnourished hemodialysis patients: a 2-year multicenter, prospective, randomized study. J Am Soc Nephrol 2007; 18: 2583–2591 Jadoul M, Ueda Y, Yasuda Y et al. Influence of hemodialysis membrane type on pentosidine plasma level, a marker of “carbonyl stress”. Kidney Int 1999; 55: 2487–2492 Acchiardo SR, Moore LW, Latour PA. Malnutrition as the main factor of morbidity and mortality in hemodialysis patients. Kidney Int 1983; 24(Suppl 16): S199–S203 Kalantar-Zadeh K, Supasyndh O, Lehn RS et al. Normalized protein nitrogen appearance is correlated with hospitalization and mortality in

A general overview of malnutrition in normal kidney function and in chronic kidney disease

57.

58.

59.

60.

61. 62.

63.

hemodialysis patients with Kt/V greater than 1.20. J Ren Nutr 2003; 13: 15–25 Bergstrom J, Heimburger O, Lindholm B. Calculation of the protein equivalent of total nitrogen appearance from urea appearance. Which formulas should be used? Perit Dial Int 1998; 18: 467–473 Canaud B, Leblanc M, Garred LJ et al. Protein catabolic rate over lean body mass ratio: a more rational approach to normalize the protein catabolic rate in dialysis patients. Am J Kidney Dis 1997; 30: 672– 679 Canaud B, Garred LJ, Argiles A et al. Creatinine kinetic modelling: a simple and reliable tool for the assessment of protein nutritional status in haemodialysis patients. Nephrol Dial Transplant 1995; 10: 1405–1410 Terrier N, Jaussent I, Dupuy AM et al. Creatinine index and transthyretin as additive predictors of mortality in haemodialysis patients. Nephrol Dial Transplant 2008; 23: 345–353 Keshaviah PR, Nolph KD, Provant B et al. Lean body mass estimation by creatinine kinetics. J Am Soc Nephrol 1994; 4: 1475–1485 Dombros N, Dratwa M, Feriani M et al. European best practice guidelines for peritoneal dialysis. 8 Nutrition in peritoneal dialysis. Nephrol Dial Transplant 2005; 20(Suppl 9): ix28–ix33 Cha K, Chertow GM, Gonzalez J et al. Multifrequency bioelectrical impedance estimates the distribution of body water. J Appl Physiol 1995; 79: 1316–1319

7

64. Chertow GM, Lowrie EG, Wilmore DW et al. Nutritional assessment with bioelectrical impedance analysis in maintenance hemodialysis patients. J Am Soc Nephrol 1995; 6: 75–81 65. Di Iorio BR, Scalfi L, Terracciano V et al. A systematic evaluation of bioelectrical impedance measurement after hemodialysis session. Kidney Int 2004; 65: 2435–2440 66. Jankowska M, Debska-Slizien A, Rutkowski B. Bioelectrical impedance analysis before versus after a hemodialysis session in evaluation of nutritional status. J Ren Nutr 2006; 16: 137–140 67. Stenver DI, Gotfredsen A, Hilsted J et al. Body composition in hemodialysis patients measured by dual-energy X-ray absorptiometry. Am J Nephrol 1995; 15: 105–110 68. Woodrow G, Oldroyd B, Smith MA et al. Measurement of body composition in chronic renal failure: comparison of skinfold anthropometry and bioelectrical impedance with dual energy X-ray absorptiometry. Eur J Clin Nutr 1996; 50: 295–301 69. Formica C, Atkinson MG, Nyulasi I et al. Body composition following hemodialysis: studies using dual-energy X-ray absorptiometry and bioelectrical impedance analysis. Osteoporos Int 1993; 3: 192–197 70. Borovnicar DJ, Wong KC, Kerr PG et al. Total body protein status assessed by different estimates of fat-free mass in adult peritoneal dialysis patients. Eur J Clin Nutr 1996; 50: 607–616 71. National Kidney Foundation. NKF-DOQI clinical practice guidelines for hemodialysis adequacy. Am J Kidney Dis 1997; 30: S15–S66 Received for publication: 3.11.08; Accepted in revised form: 20.8.09